code: def sample_from_truncated_normal_distribution(
      self, lower, upper, mu, sigma, size=1
  ):
      from scipy import stats
  
      return stats.truncnorm.rvs(
          (lower - mu) / sigma,
          (upper - mu) / sigma,
          loc=mu,
          scale=sigma,
          size=size,
      )
  
imports: import numpy
  import torch
  import torch.nn
  from torch.optim import Adam
  from .memory import SequentialMemory
  from .utils import to_numpy, to_tensor
label_desc: Use image node_id 13 for calling the DDPG obj's underlying member method code with example usage: obj.sample_from_truncated_normal_distribution(lower, upper, mu, sigma, size) and returns: stats